The present embodiments relate to motion compensation in magnetic resonance imaging. Patient motion may degrade the image quality in magnetic resonance imaging (MRI) and cause unwanted image blurring or ghosting. More than 8% of magnetic resonance (MR) scans may be subject to motion, resulting in impaired diagnostic quality.
The undesired effects of patient motion during MRI may result, in part, from the Fourier transform (FT) of the acquired k-space data. Any inconsistency in the k-space caused by motion may influence many or every pixel in the image domain.
Prospective or retrospective methods have been used to account for motion. Prospective approaches, such as breath-holding, ECG monitoring, or respiratory gating, require extra clinical set-up and often suffer from limited performance. The patient may fail to hold their breath for sufficient time. Prospectively adjusting the image acquisition is often not possible due to scan time and contrast limits or difficulties to modify the MR imaging sequences. The prospective techniques mainly deal with rigid or affine type motion, which is insufficient for applications like cardiac or liver imaging. The retrospective methods include retrospective gating, which sacrifices the image efficiency by rejecting acquired data if significant motion is detected.
MR reconstruction may incorporate motion compensation to cope with rigid motion or non-rigid deformation. If the MR data acquisition is performed in a multi-shot or multi-segment manner and the motion between each shot is known, the motion compensation may be achieved by solving a general matrix inversion problem. Although the theory of conducting multi-shot MR motion compensation using general matrix inversion has been established, the application of this framework is rare, mainly due to the difficulties of estimating a complete displacement field for every pixel in the image at every shot. An attempt to bypass this problem provides a model of motion within the field of view. The model is parameterized as a linear combination of selected input basis signals, such as navigator echoes or respiratory belts or signals derived from ECG. The optimization process is extended to interleave the estimation of a motion-free image and motion model by solving two large linear equations consecutively. While avoiding the estimation of deformation fields, this approach complicates the matrix representation of MR motion compensation and couples the motion estimation and compensation. The computational costs are largely increased, leading to reconstruction time of the order of 10 minutes.